Andrzej Rybarczyk

Real-time shape description system based on MPEG-7 descriptors

The paper presents a real-time shape description system based on MPEG-7 standard descriptors. The proposed system architecture is used for description in real-time image objects shape basing on features such as contour and occupied region. The proposed hardware architecture splits the computational burden into several processes where calculations of mentioned objects features are made simultaneously in order to improve systems speed. A novel approximation method for estimation of peaks height in the curvature scale space (CSS) representation of the shape as well as the artificial neural network (ANN) used in system development process for generalization of the data samples received from the approximation module. These methods make hardware realizations of main computational-consuming modules of the system more time efficient.

Asphalt surfaced pavement cracks detection based on histograms of oriented gradients

Cracks are the most requiring type of pavement distresses to detect and classify automatically. Due to its nature are easily absorbed by other types of pavement surface damages. Moreover, the diversity of pavement surface makes the image detection system requiring efficient computer algorithms. The paper presents the solutions tested on surface distress data which were collected automatically using downward facing cameras placed orthogonally to road pavement axis. Presented results focus on the crack-type pavement distresses. The achieved accuracy of the transverse, longitudinal and meshing cracks recognition based on the initial dataset prepared especially for this system, show it has very good chances to work efficiently with large image datasets collected during the inspection car runs.

Real-time event classification in field sport videos

The paper presents a novel approach to real-time event detection in sports broadcasts. We present how the same underlying audio-visual feature extraction algorithm based on new global image descriptors is robust across a range of different sports alleviating the need to tailor it to a particular sport. In addition, we propose and evaluate three different classifiers in order to detect events using these features: a feed-forward neural network, an Elman neural network and a decision tree. Each is investigated and evaluated in terms of their usefulness for real-time event classification. We also propose a ground truth dataset together with an annotation technique for performance evaluation of each classifier useful to others interested in this problem. HighlightsArticle presents novel system for real-time video indexing for field sports videos based on the new video description technique.Up to date there is no system that covers so many different sports genres, features so high accuracy and is capable of working in real-time.Several classifiers were compared in the results section which gives additional knowledge about the system features.

Hardware Realization Of The MPEG-7 Edge Histogram Descriptor

The paper presents hardware implementation of the MPEG-7 edge histogram descriptor. The testing circuit was described using VHDL language and synthesized into FPGA. The RC1000 board with a Xilinx Virtex V1000 FPGA was chosen as the target platform. Experimental results of the descriptor efficiency are presented too

Random number generation system improving simulations of stochastic models of neural cells

The purpose of this work is to speed up simulations of neural tissues based on the stochastic version of the Hodgkin–Huxley model. Authors achieve that by introducing the system providing random values with desired distribution in simulation process. System consists of two parts. The first one is a high entropy fast parallel random number generator consisting of a hardware true random number generator and graphics processing unit implementation of pseudorandom generation algorithm. The second part of the system is Gaussian distribution approximation algorithm based on a set of generators of uniform distribution. Authors present hardware implementation details of the system, test results of the mentioned parts separately and of the whole system in neural cell simulation task.

The concept of a microcontroller with neural-matrix coprocessor for control systems that exploits reconfigurable FPGAs

The new digital architecture of specialized microcontroller with neural coprocessor for efficient real time control systems of robots is presented. The main idea of the paper is to present the on-chip integrated core of the popular microcontroller, program and data memories and the neural-matrix coprocessor. In order to explain the design, the main processor, neural coprocessor and accompanied networks are described. The reconfigurable FPGA matrix has been used as the prototyping platform. It made possible the fast prototyping process. The paper describes also the future work to implement the presented system as ASIC chip.

Multisensor data fusion using Elman neural networks

The paper presents a navigation system based on Elman Artificial Neural Network (ANN). The task of data fusion from different sensors is realized by trained ANN. Determining position in space is an issue of nonlinear hence. Not every type of ANN is used for such a task. Choice of Elman ANN was dictated by its construction and successfully applications to nonlinear problems requiring prediction. Elman network is composed of three layers. Comprises a layer of hidden layer units context which is connected to the hidden layer. Context-sensitive layer allows for store the values of previous hidden units. With this layer prediction is possible in sequential order. This is the effect of contextual memory where information is stored about what it was before. This kind of functionality is not able to provide any other standard neural network unidirectional. The system consists of MEMS (Micro Electro-Mechanical Systems) sensors, which are based on IMU (Inertial Measurement Unit). IMU is composed from gyroscopes, accelerometers and magnetometers which provide three dimensional linear accelerations and angular rates. This is a classic set of sensors for determining the position in space. The study presents the results of the implementation of algorithms for determining the position in space using trained Elman ANN. The data samples to train ANN were collected during the test flight of Quadrocopter. Paper presents the performance for different configurations of Elman ANN. Presented system provides easy addition of other sensors e.g. GPS/GLONASS receiver.

The Linux As The Operating System For Computational Node Of Custom Computing Machine Class System

The paper presents the application of the modified real-time Linux kernel as the operating system for the computational node of CCM (custom computing machine) class parallel system designed to aid the simulations of three-dimensional electromagnetic field. The computational node is the application-specific design optimized for the implementation in the FPGA. It is based on Xilnx VirtexIIPro FPGA equipped with two embedded cores of the PowerPC processors. The presented real-time kernel is designed to run on one of the cores. The paper details the procedure needed to create the kernel customized for the specific architecture of the system. The software packages and tools utilized in the kernel development process are also presented

The CCM Based Implementation of the Parallel Variant of BiCG Algorithm Suitable for Massively Parallel Computing

The paper presents the implementation of a method for solving the large sparse non-symmetric linear system on the CCM class parallel machine. The method is an adaptation of the well-known biconjugate gradient algorithm. The equations solved results from the FEM based computations of the 3D electromagnetic field with a motion taken into account

Trajectory realization with collision avoidance algorithm

This paper describes the approach to collision avoidance problem for 3-DOF anthropomorphic robot manipulators. The novelty of the approach is the decomposition of 3D space to two 2D spaces. Resulting is the computationally efficient algorithm, suitable for implementation in the real-time systems. Simulation of the anthropomorphic manipulator operating in three dimensional space with obstacles is also presented.